There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment.
This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix. While vertex-centric systems have gained great popularity, the latter two models are currently being actively studied to solve graph problems that cannot be efficiently solved in vertex-centric model, and are the promising next-generation models for big graph analytics. For each part, the authors introduce the state-of-the-art systems, emphasizing on both their technical novelties and hands-on experiences of using them. The systems introduced include Giraph, Pregel+, Blogel, GraphLab, CraphChi, X-Stream, Quegel, SystemML, etc.
Readers will learn how to design graph algorithms in various graph analytics systems, and how to choose the most appropriate system for a particular application at hand. The target audience for this book include beginners who are interested in using a big graph analytics system, and students, researchers and practitioners who would like to build their own graph analytics systems with new features.
| ISBN: | 9783319582160 |
| Publication date: | 13th June 2017 |
| Author: | Yan Da, Yuanyuan Tian, James Cheng |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 92 pages |
| Series: | SpringerBriefs in Computer Science |
| Genres: |
Network hardware Graphics programming |
There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment.This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix.
Systems for Big Graph Analytics features in the following genres: Network hardware, Graphics programming
Paperback. Not Available.
Systems for Big Graph Analytics was written by Yan Da, Yuanyuan Tian, James Cheng and published by Springer an imprint of Springer International Publishing
Systems for Big Graph Analytics has 92 pages
Yes it is part of SpringerBriefs in Computer Science series